arviz | Exploratory analysis of Bayesian models with Python | Analytics library
kandi X-RAY | arviz Summary
kandi X-RAY | arviz Summary
ArviZ (pronounced "AR-vees") is a Python package for exploratory analysis of Bayesian models. Includes functions for posterior analysis, data storage, model checking, comparison and diagnostics.
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Top functions reviewed by kandi - BETA
- Concatenates inference data .
- Generate a summary of the data .
- Plot a density matrix .
- Plot a posterior distribution .
- Plot a LM model .
- Plot an energy distribution .
- Plot a forest plot .
- Plot posterior distribution .
- Plot a posterior probability distribution .
- Plot a pairwise pair .
arviz Key Features
arviz Examples and Code Snippets
Lines of Code : 5License : Strong Copyleft (CC BY-SA 4.0)
Copyimport arviz
grid, pdf = arviz.kde(trace.posterior.X.values, cumulative=True)
idx = np.sum(grid < 0) - 1
prob_x_lt_zero = pdf[idx]
import arviz; import xarray
x = xarray.DataArray([-.1, 0, .1]) # skip that if working with scalars
post = arviz.load_arviz_data("rugby").posterior
prob_x_lt_zero = (post.atts < x).mean(("chain", "draw"))
arrayconda install python-snappy
pip install python-snappy
with model:
...
samp = fit.sample(1500)
idata = az.from_pymc3(samp)
az.summary(idata.posterior[["b"]].sel({"b_dim_0": [0]}))
c = 2.0
with pm.Model() as my_model:
a = pm.Normal("a", -1, 0.1)
b = pm.Normal("b", 15.0, 2.0)
Y_mu = pm.Deterministic("Y_mu", a + b + c)
Y_sigma = pm.Normal('Y_sigma', mu=1, sd=1)
Y = pm.Normal("Y", mu=Y_mu,P[x=0|Normal[0,1]] = 1/sqrt(2*pi) = 0.3989422804014327
P[x=0| Zero ] = 1
with pm.Model() as model:
norm = pm.Normal.dist(0.0, 1.0)
pseudo_zero = pm.Laplace.dist(0.0, 1e-16)
components = [norm,import arviz as az
az.plot_posterior(trace, kind="hist")
with pm.Model() as m_adult:
weight_s = pm.Data("weight_s", adults.weight_s.values)
a = pm.Normal("α", mu=155, sd=20)
b = pm.Lognormal("β", mu=0, sd=1)
mu = pm.Deterministic("μ", a + b * weight_s)
sigma = pm.Uniform("σ"az.plot_trace(trace, compact=True)
A["x"].values
# or what is the same A.x.values
Community Discussions
Trending Discussions on arviz
QUESTION
I have a sample from PyMC3 and I'm trying to get a cumulative probability from it, e.g. P(X < 0). I currently use this:
...ANSWER
Answered 2022-Jan-12 at 11:32You could approximate the CDF with a kernel density estimate, but I am not convinced that this is better than your current approach:
QUESTION
I'm trying to include a black box likelihood function in a pymc3 model. This likelihood function just takes a vector of parameter values and returns the likelihood (all data is already included in the function).
So far I've been following this guide and have modified the code as follows to accommodate the fact my model only has one parameter k.
...ANSWER
Answered 2021-Nov-05 at 09:42As per the comments I checked out this thread and discovered that pm.potential really was the cleanest way to achieve black-box likelihood. Modifying the code above as follows did the trick:
QUESTION
data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data
I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.
...ANSWER
Answered 2021-Oct-11 at 14:21geopandas 0.10.1
- have noted that your data is on kaggle, so start by sourcing it
- there really is only one issue
shapely.geometry.MultiPoint()constructor does not work with a filtered series. Pass it a numpy array instead and it works. - full code below, have randomly selected a point to serve as
gpdPoint
QUESTION
After installing ArviZ in the obvious way (conda install -c conda-forge arviz), I get the following failure. If anyone could shed light on what to do to fix this, that would be great.
ANSWER
Answered 2021-Aug-26 at 16:37Looking at the installed packages, you don't have python-snappy installed. I just verified that installing it seems to get it working, although you are right that technically python-snappy and snappy are colliding into the module namespace snappy.
QUESTION
I am estimating a model using the pyMC3 library in python. In my "real" model, there are four parameter arrays, two of which have over 170,000 parameters in them. Summarising this array of parameters is too computationally intensive on my computer. I have been trying to figure out if the summary function in arviz will allow me to only summarise one (or a small number) of parameters in the array. Below is a reprex where the same problem is present, though the model is a lot simpler. In the linear regression model below, the parameter array b has three parameters in it b[0], b[1], b[2]. I would like to know how to get the summary for just b[0] and b[1] or alternatively for just a single parameter, e.g., b[0].
ANSWER
Answered 2021-May-28 at 15:34To use coords for this, you need to update to the development (which will still show 0.11.2 but has the code from github or any >0.11.2 release) version of ArviZ. Until 0.11.2, the coords argument in summary was not used to subset the data (like it did in all plotting functions) but instead it was only taken into account if the input was not already InferenceData in which case it was passed to the converter.
With older versions, you need to use xarray to subset the data before passing it to summary. Therefore you need to explicitly convert the trace to inferencedata beforehand. In the example above it would look like:
QUESTION
I have a linear model of a system, for which I don't currently have any data. It could be of a form Y = a + b + c. I would like to simulate likely values of Y based on assumptions about the distribution of the input parameters. It could be that a is distributed as Normal(mu=-1, sd=0.1), b as Normal(mu=15.0, sd=2.0) and c is a constant. I have tried to implement such a simulation using pyMC3 as follows:
ANSWER
Answered 2021-May-19 at 00:37You have to use pm.Deterministic to store Y_mu as a variable in the trace:
QUESTION
I have data that is a 50:50 mix of a normal distribution and a constant value:
...ANSWER
Answered 2021-Mar-07 at 03:45The issue here is that the likelihood of coming from each model involves probability density for the Gaussian and mass for the discrete, which are not commensurate. Specifically, the computation for comparing where a zero observation came from, will involve likelihoods
QUESTION
I have an old blogpost where I am training a PyMC3 model. You can find the blogpost here but the gist of the model is shown below.
...ANSWER
Answered 2020-Oct-26 at 08:00There's a parameter for it in the new plot_trace function. This does the trick;
QUESTION
I am trying to perform Bayesian logistic regression using pymc3, but I am facing an issue in using the model to perform prediction.
Data:
My dataset is of the housing loan default data, with sample data as follows:
...ANSWER
Answered 2020-Sep-17 at 15:33If you replace the code between your # model training (error cause) and # AUC comments with the below you should be able to run it and start getting some results:
QUESTION
I am new to PyMC3 and Bayesian inference methods. I have a simple code that tries to infer the value of some decay constant (=1) from the artificial data generated using a truncated exponential distribution:
...ANSWER
Answered 2020-Sep-10 at 07:22Few things going on:
- when the sampler first starts it has a tuning phase; samples during this phase are discarded by default, but this can be controlled with the
discard_tuned_samplesargument - the keys in the
startargument dictionary need to correspond to the name given to the RandomVariable ('$\lambda$') not the Python variable
Incorporating those two, one can try
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install arviz
You can use arviz like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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